Mapping IPC competencies among nurses
Description
We hypothesized that nurses in India may not be fully aligned with global infection prevention and control (IPC) competencies, and that gaps in knowledge, attitudes, and practices (KAP) would highlight the need for structured, evidence-based training and certification. The dataset includes responses from 547 nurses across India and provides detailed insights into their IPC competencies. Overall, nurses showed average levels of knowledge and practice but lower scores in attitude. However, when scores were categorized as low, average, or good, the majority of nurses (over 70%) fell into the low category for practice, despite the mean practice score appearing average. This suggests that a small group of high performers shifted the mean upward, masking widespread deficiencies and underscoring a critical disconnect between awareness and actual implementation. Demographic variables, such as years of experience and training background, were significantly associated with variation in IPC competencies. Predictors: Ordinal logistic regression identified demographic and professional predictors of stronger knowledge and practice, suggesting priority groups for targeted interventions. Geographic Spread: State-wise mapping revealed wide distribution of respondents, enhancing the generalizability of findings. How to Interpret and Use the Data: Raw data excel with scoring for correct answer Supplementary Table S1 details all KAP items with correct/preferred responses and percentage of correct answers, enabling replication and cross-country comparisons. Supplementary Table S2 presents associations between demographic variables and KAP levels, providing context for training policy development. Supplementary Table S3 summarizes regression models to identify predictors of better IPC performance, guiding targeted educational interventions. Supplementary Figures 1 and 2 visualize geographic representation and predicted probabilities of practice levels, supporting stratified interpretation. Together, these data provide a foundation for designing tailored IPC education and certification programs for nurses in India, with implications for improving patient safety, reducing healthcare-associated infections (HAIs), and enhancing preparedness for both routine and emergency healthcare delivery.
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Data Collection Overview Study Design: Cross-sectional, questionnaire-based survey. Population: Registered nurses across India. Recruitment: The survey link was initially circulated to a known group of nurses via professional networks, email groups, and social media, and further disseminated using a snowball sampling approach. Only nurses were invited to participate. Instrument Development Questionnaire Basis: A structured, self-administered questionnaire was designed using guidelines from the World Health Organization’s Infection Prevention and Control Assessment Framework (IPCAF) (WHO, 2018) and the Centers for Disease Control and Prevention’s Core Infection Prevention and Control Practices (CDC, 2024). Sections: Knowledge (21 items): Focused on standard precautions, transmission-based precautions, device reprocessing, and environmental cleaning. Attitude (16 items): Explored perceptions related to device-associated infection prevention, PPE use, and antimicrobial stewardship. Practice (23 items): Evaluated self-reported frequency and accuracy of behaviors such as hand hygiene, PPE use, waste management, and risk assessment. Validation: The questionnaire was reviewed by IPC experts and piloted with a small group of nurses for clarity and relevance. Data Collection Process Mode: Online, anonymous, self-administered survey hosted on [insert platform, e.g., Google Forms/SurveyMonkey]. Timeframe: 2 months Consent: Informed consent was obtained electronically before participation. Data Management and Analysis Scoring: Responses were coded as correct/incorrect (knowledge, practice) or favorable/unfavorable (attitude). Scores were converted into percentages and categorized as low (<50%), average (50–79%), and good (≥80%). Software: Data were exported into Microsoft Excel and analyzed using SPSS version [insert version]. Statistical Analysis: Descriptive statistics, chi-square tests, and ordinal logistic regression were performed to identify predictors of IPC KAP scores.
Institutions
- Institute of Human Behaviour and Allied Sciences